Through academic excellence, we provide the theoretical foundation for critical thinking in quantitative problem solving and reasoning. We help students to develop their ability to effectively communicate mathematics, and prepare them for success in a career in actuarial mathematics, applied mathematics, applied statistics, or other profession.


Department Faculty

Adjunct Professors

  • Patricia Capuano
  • David Deacon
  • Gerard Ethier
  • JoAnn Lovett
  • David Mellor
  • Bradford Monroe
  • Eric Simonian
  • Steven Toce
  • Denise Trudeau
  • William Velle
  • Suzanne Walker
  • James Wood
Lamere, Alicia

Alicia T. Lamere

Assistant Professor, Mathematics
Faculty Suite A, Room A11
Ph D, University of Notre Dame
MS, University of Notre Dame
BA, Hamilton College
  • Caucus for Women in Statistics
  • Kappa Mu Epsilon
  • Mu Sigma Rho
  • Association of Women in Science
  • American Statistical Association

Lamere, A. T., Li, J., Inference of gene co-expression networks from single-cell data, Springer Protocols.

Nguyen, S., Lamere, A. T., Olinsky, A., Quinn, J., The Effects of Sampling Methods on Machine Learning Models for Predicting Long-term Length of Stay: A Case Study of Rhode Island Hospitals, International Journal of Rough Sets and Data Analysis (IJRSDA)/IGI Publishing.

Specht, A. T., Widespread position-specific conservation of synonymous rare codons within coding sequences, PLoS Computational Biology, 2017.

Specht, A. T., LEAP: Constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering, Bioinformatics, 2016.

Specht, A. T., Estimation of gene co-expression from RNA-Seq count data, Statistics and Its Interface, 2015.